Preface
An emerging trend in many scientific disciplines is a strong tendency toward being transformed into some form of information science. One important pathway in this transition has been via the application of network analysis. The basic methodology in this area is the representation of the structure of an object of investigation by a graph representing a relational structure. It is because of this general nature that graphs have been used in many diverse branches of science including bioinformatics, molecular and systems biology, theoretical physics, computer science, chemistry, engineering, drug discovery, and linguistics, to name just a few. An important feature of the book “Statistical and Machine Learning Approaches for Network Analysis” is to combine theoretical disciplines such as graph theory, machine learning, and statistical data analysis and, hence, to arrive at a new field to explore complex networks by using machine learning techniques in an interdisciplinary manner.
The age of network science has definitely arrived. Large-scale generation of genomic, proteomic, signaling, and metabolomic data is allowing the construction of complex networks that provide a new framework for understanding the molecular basis of physiological and pathological states. Networks and network-based methods have been used in biology to characterize genomic and genetic mechanisms as well as protein signaling. Diseases are looked upon as abnormal perturbations of critical cellular networks. ...
